FIDAP capabilities for solving problems with stiff chemistry

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In support of the Motorola CRADA, the capabilities of the computational fluid dynamics code FIDAP (Fluid Dynamics International) for simulating problems involving fluid flow, heat transport, and chemical reactions have been assessed and enhanced as needed for semiconductor-processing applications (e.g. chemical vapor deposition). A novel method of treating surface chemical species that uses only pre-existing FIDAP commands is described and illustrated with test problems. A full-Jacobian treatment of the chemical reaction rate expressions during formation of the stiffness matrix has been implemented in FIDAP for both the Arrhenius-parameter and user-subroutine methods of specifying chemical reactions, where the Jacobian terms can ...
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Description

In support of the Motorola CRADA, the capabilities of the computational fluid dynamics code FIDAP (Fluid Dynamics International) for simulating problems involving fluid flow, heat transport, and chemical reactions have been assessed and enhanced as needed for semiconductor-processing applications (e.g. chemical vapor deposition). A novel method of treating surface chemical species that uses only pre-existing FIDAP commands is described and illustrated with test problems. A full-Jacobian treatment of the chemical reaction rate expressions during formation of the stiffness matrix has been implemented in FIDAP for both the Arrhenius-parameter and user-subroutine methods of specifying chemical reactions, where the Jacobian terms can be calculated analytically or numerically. This formulation is needed to obtain convergence when reaction rates become large compared to transport rates (stiff chemistry). Several test problems are analyzed, and in all cases this approach yields good convergence behavior, even for extremely stiff fluid-phase and surface reactions. A stiff segregated algorithm has been developed and implemented in FIDAP. Analysis of test problems indicates that this algorithm yields improved convergence behavior compared with the original segregated algorithm. This improved behavior enables segregated techniques to be applied to problems with stiff chemistry, as required for large three-dimensional multi-species problems.

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